Summary Alternative splicing is nearly universal in human genes, producing multiple distinct mRNAs and proteins from each individual gene locus. This process is regulated by over one hundred splicing factors that bind to specific RNA motifs in the primary transcript and modulate splicing by interaction with core splicing machinery or with other splicing factors. This proposal seeks to understand the rules that govern the activities of splicing factors. Each splicing factor's activity can be summarized by an RNA map that describes how its activity depends on location of binding relative to the regulated exon or splice sites. It is organized around the following aims. SA1. To develop and test second-generation (2G) “RNA Maps” describing splicing factor activity SA2. To understand the protein sequence determinants of splicing regulatory activity and improve RNA maps using engineered splicing factors SA3. To improve RNA maps by incorporating indirect and interaction effects In Aim 1, we will develop models that distinguish notions of “affinity”, “binding”, “location”, and “regulatory activity”, and will develop software to generate 2G RNA maps from different combinations of data types, including in vitro and/or in vivo binding data and RNA sequencing data, and will extend these maps to several types of RNA processing events. In Aim 2 we will identify protein sequence features that confer different types of splicing regulation, and will engineer “hyperactive” splicing factors to produce larger splicing changes and improve functional inference. Finally, we will dissect direct regulation by a factor from indirect regulation – where one splicing factor regulates another that directly regulates the splicing of other genes – and will also consider how splicing factors may cooperate or antagonize one another's regulatory activity. Together, these studies will improve our understanding of how RNA splicing factors work, enabling improved understanding of disease states where splicing factors are mutated (including many blood cancers), and improved prediction of the effects of sequence variants in the human genome that cause disease by altering splicing factor binding sites in exons and introns.